• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1137-1140
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• 1997

This paper presents a fuzzy-neural network controller technique on the load frequency control of two-area power system. Firstly, Fuzzy controller a series of initial selected rules are improved by means of the proposed technique. Secondly, scale factors for error, change rate of error and control input are optimized by the given error back-pagation teaming algorithms. Finally, the related simulation results show that the proposed fuzzy neural network controller technique are more powerful than conventional ones.

• 한국정밀공학회지
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• 제14권1호
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• pp.101-107
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• 1997

Recently, as the raw water quality becomes to be polluted and the seasonal and local variation of water quality becomes to be severe, an exact control of coagulant dosing have been required in the water treat- ment plant. The amounts of coagulant is related to the raw water quality such as turbidity, alkalinity, water temperature, pH and edectrical conductivity. However the process of chemical reaction has not been clarified so far, so the dosing rate has been decided by jar-test, which is taken one or two hours. For the sake of this coagulant dosing control, fuzzy neural network to fuse fuzzy logic and neural network was proposed, and the scheme was applied to automatic determination of coagulant dosing rate. This controller can automatically identify the if-then rules and tune the membership functions by utilizing expert's cintrol data. It is shown that determination of coagulant dosing rate according to real time sensing of water quality is very effect.

• 한국정보통신학회논문지
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• 제5권5호
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• pp.977-984
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• 2001

본 논문에서는 유전자-퍼지 제어 알고리즘에 대하여 논의하고 그 성능을 평가하였다. 이 알고리즘은 퍼지 논리와 유전자알고리즘의 융합된 형태이며, 제어 대상으로는 도립진자 시스템을 모델링 하였다. 퍼지 제어기는 두 개의 입력과 한 개의 출력 변수를 설계하기 위해 적용되며, GA(Genetic Algorithm)는 퍼지 규칙과 소속 함수를 선택, 교차, 돌연변이의 진화 연산을 통해 최적화한다. 컴퓨터 시뮬레이션에 퍼지 제어의 경우 초기 함수 f(0.3, 0.3)일 때 최대 언더슈트가 $-5.0 \times 10^{-2}[rad]$, 최대 오버슈트가 $3.92\times10^{-2}[rad]$으로 측정되었으나, 유전자 퍼지 알고리즘의 경우 최대 오버슈트와 언더슈트가 각각 0.0[rad]으로 측정되었다. 또한 정상상태 도달시간이 퍼지제어의 경우 2.12[sec], 유전자-퍼지 알고리즘은 1.32[sec]로 비교적 안정적으로 나타났다. 컴퓨터 시뮬레이션으로 이 알고리즘을 도립진자 시스템에 적용시키고, 그 성능의 우수성과 효율성을 증명하였다.

• International Journal of Automotive Technology
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• 제8권5호
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• pp.651-658
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• 2007

This paper presents a new type of fuzzy logic-based power control strategy for fuel cell hybrid electric vehicles designed to improve their fuel economy while maintaining the battery's state of charge. Since fuel cell systems have inherent limitations, such as a slow response time and low fuel efficiency, especially in the low power region, a battery system is typically used to assist them. To maximize the advantages of this hybrid type of configuration, a power distribution control strategy is required for the two power sources: the fuel cell system and the battery system. The required fuel cell power is procured using fuzzy rules based on the vehicle driving status and the battery status. In order to show the validity and effectiveness of the proposed power control strategy, simulations are performed using a mid-size vehicle for three types of standard drive cycle. First, the fuzzy logic-based power control strategy is shown to improves the fuel economy compared with the static power control strategy. Second, the robustness of the proposed power control strategy is verified against several variations in system parameters.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.12-18
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• 2010

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권5호
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• pp.249-256
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• 2000

This paper deals with the control of the lateral motion of a mobile vehicle. A mobile vehicle using in this experiment is able to adapt many unmanned automatic driving system, for example, like a automated product transporting system. This vehicle is consist of the two servomotors. One is used to accelerate this vehicle and the another is used to change this lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral motion of the vehicle. Therefore, the main aim of this paper is investigate the possibility of applying adaptive fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. Fuzzy control rules are derived by modelling an expert's driving actions. Experiments are performed using a mobile vehicle with sensing units, a microprocessor and a host computer.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.299-304
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• 1997

A neuro-fuzzy control algorithm is applied for xenon spatial oscillations in a pressurized water reactor. The consequent and antecedent parameters of the fuzzy rules are tuned by the gradient descent mettled. The reactor model used for computer simulations is a two-point xenon oscillation model. The reactor core is axially divided into two regions and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control of mettled exhibits very fast responses to a step or a ramp change of target axial offset without any residual flux oscillations.

• Journal of Advanced Marine Engineering and Technology
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• 제36권8호
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• pp.1104-1110
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• 2012

도립진자 시스템은 제어이론의 다양한 기본 요소를 포함하는 제어문제를 다루는데 많이 사용되고 있다. 초기에 도립진자 시스템의 안정화 제어는 PD, PID 등의 고전적인 방법이 주로 사용되었으나 최근에는 현대제어 이론과 지능제어 기법을 이용한 방법이 주로 적용된다. 최근 비선형 제어에 종종 사용되는 퍼지제어기는 도립진자 시스템과 같이 시스템의 상태 변수가 많으면 퍼지 규칙은 급격하게 증가되어 설계가 까다롭게 된다. 또한 상태 변수를 2개씩 구분하여 퍼지제어기를 설계하면 하나의 제어시스템에 2개의 퍼지제어기를 설계해야 하는 번거로움이 발생한다. 본 논문에서는 이러한 문제점을 해결하기 위해 도립진자 시스템의 4개의 상태변수를 물리적 의미에 따라 새로운 2개의 신호로 재구성하고, 이를 퍼지 입력 변수로 사용하는 상태변수 조합 퍼지 제어기(FCSC)를 제안한다. 제안한 제어기는 컴퓨터 시뮬레이션을 통해 그 유효성을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2264-2269
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• 2003

This paper presents an improved method that constructs an equivalent T-S fuzzy model for nonlinear systems expressed by nonlinear differential equations including terms of power series. The method in this paper has fewer numbers of the rules than the previous methods as well as exactly expresses nonlinear systems. Moreover, this method can get wider feasible area satisfying the stability conditions than the previous methods. We show the improvement of modeling by comparing the proposed method with two previous methods through an inverted pendulum on a cart.